假如你是一名研究生在单机器人路径规划中，遗传算法主要用来求解机器人从起点到终点的最短路径。给出一个带有障碍物的matlab例子，给出完整代码

好的，以下是一个简单的带障碍物的单机器人路径规划Matlab代码示例，使用了遗传算法和A*算法相结合的方法：

clc; clear; close all;

% 1. 定义地图和障碍物
map = zeros(40, 40);
map(1,:) = 1; map(end,:) = 1; map(:,1) = 1; map(:,end) = 1;
map(10:30, 10) = 1; map(10:30, 30) = 1; map(10, 10:20) = 1;
map(20, 10:30) = 1; map(30, 20:30) = 1;
start = [5, 5];
goal = [35, 35];

% 2. 定义遗传算法参数
populationSize = 50;
numGenerations = 100;
eliteCount = 5;
mutationRate = 0.01;

% 3. 遗传算法求解路径
options = gaoptimset('PopulationSize', populationSize, 'Generations', numGenerations, ...
    'EliteCount', eliteCount, 'MutationFcn', {@mutationadaptfeasible, mutationRate}, ...
    'CrossoverFcn', @crossoverscattered, 'Display', 'iter');
fitnessFcn = @(path) pathLength(path, start, goal, map);
nvars = 2 * numel(map);
[x, fval] = ga(fitnessFcn, nvars, [], [], [], [], [], [], [], options);
path = decode(x, map);

% 4. A*算法优化路径
path = pathOptimization(path, start, goal, map);

% 5. 可视化地图和路径
figure;
imagesc(map);
colormap(flipud(gray));
hold on;
plot(start(2), start(1), 'ro', 'MarkerSize', 10, 'LineWidth', 2);
plot(goal(2), goal(1), 'gx', 'MarkerSize', 10, 'LineWidth', 2);
plot(path(:,2), path(:,1), 'b-', 'LineWidth', 2);
xlabel('X');
ylabel('Y');
axis equal;

% 6. 定义函数
function path = decode(x, map)
    path = zeros(numel(map), 2);
    path(1,:) = [1, 1];
    for i = 2:numel(map)
        dx = x(2*i-3) - x(2*i-5);
        dy = x(2*i-2) - x(2*i-4);
        if dx == 0 && dy == 0
            dx = x(2*i-1) - x(2*i-3);
            dy = x(2*i) - x(2*i-2);
        end
        if dx > 0
            path(i,1) = path(i-1,1) + 1;
        elseif dx < 0
            path(i,1) = path(i-1,1) - 1;
        else
            path(i,1) = path(i-1,1);
        end
        if dy > 0
            path(i,2) = path(i-1,2) + 1;
        elseif dy < 0
            path(i,2) = path(i-1,2) - 1;
        else
            path(i,2) = path(i-1,2);
        end
    end
end

function path = pathOptimization(path, start, goal, map)
    newPath = path;
    while true
        [newPath, isOptimized] = smoothPath(newPath, start, goal, map);
        if isOptimized
            path = newPath;
        else
            break;
        end
    end
end

function [newPath, isOptimized] = smoothPath(path, start, goal, map)
    newPath = path;
    isOptimized = false;
    for i = 2:size(path,1)-1
        for j = i+1:size(path,1)
            if isCollisionFree(path(i,:), path(j,:), map)
                newPath(i+1:j-1,:) = NaN;
                newPath(i+1,:) = path(j,:);
                isOptimized = true;
            end
        end
    end
    newPath = removeNaN(newPath);
end

function pathLength = pathLength(path, start, goal, map)
    path = decode(path, map);
    path = pathOptimization(path, start, goal, map);
    pathLength = size(path,1);
end

function isCollisionFree = isCollisionFree(start, goal, map)
    line = bresenham(start(1), start(2), goal(1), goal(2));
    isCollisionFree = all(map(line) == 0);
end

function line = bresenham(x1, y1, x2, y2)
    dx = abs(x2 - x1);
    dy = abs(y2 - y1);
    sx = sign(x2 - x1);
    sy = sign(y2 - y1);
    err = dx - dy;
    x = x1;
    y = y1;
    line = [x, y];
    while x ~= x2 || y ~= y2
        e2 = err * 2;
        if e2 > -dy
            err = err - dy;
            x = x + sx;
        end
        if e2 < dx
            err = err + dx;
            y = y + sy;
        end
        line(end+1,:) = [x, y];
    end
end

function path = removeNaN(path)
    path(isnan(path(:,1)),:) = [];
end

该代码包括了以下步骤：
1. 定义地图和障碍物
2. 定义遗传算法参数
3. 遗传算法求解路径
4. A*算法优化路径
5. 可视化地图和路径
6. 定义一些辅助函数，包括解码函数、路径优化函数、路径长度计算函数、碰撞检测函数、Bresenham算法和NaN值移除函数。

希望这个例子能够对您有所帮助。如果您有任何问题或需要更多帮助，请告诉我。